Deep Learning of DESI Mock Spectra to Find Damped Lyα Systems
Abstract
We have updated and applied a convolutional neural network (CNN) machine learning model to discover and characterize damped Lyα systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99\% for spectra which have signal-to-noise (S/N) above 5 per pixel. Classification accuracy is the rate of correct classifications. This accuracy remains above 97\% for lower signal-to-noise (S/N) ≈1 spectra. This CNN model provides estimations for redshift and HI column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 per pixel. Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of Baryon Acoustic Oscillation (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61\% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7\%. We also compared the performance of CNN and Gaussian Process (GP) model. Our improved CNN model has moderately 14\% higher purity and 7\% higher completeness than an older version of GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24\% less standard deviation. A credible DLA catalog for DESI main survey can be provided by combining these two algorithms.